Online algorithms: a survey

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How researchers see this paper

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Learning to Run Heuristics in Tree Search (2017, methodology): “To analyze online algorithms, we resort to worst-case analysis using the competitive ratio [Albers, 2003].”
Online Learning: A Comprehensive Survey (2018, background): “Readers are also encouraged to read some older related books, surveys and tutorial notes about online learning and online algorithms (Fiat and Woeginger, 1998; Bottou, 1998b; Rakhlin, 2008; Blum, 1998; Albers, 2003).”
A unified framework for stochastic optimization (2019, background): “In practice, the field of nline algorithms refers to procedures that do not even attempt to pproximate the future, which means they are some form of mypic policy (see Borodin & El-Yanniv, 1998 for a nice introduction nd ( Albers, 2003 ) for a survey).”
Interactive and Deterministic Data Cleaning (2016, background): “Such a problem is typically categorized under the framework of online algorithms [3], where one can process input piece-by-piece in a serial fashion (i.”
A survey on metaheuristics for stochastic combinatorial optimization (2009, methodology): “This way of evaluating algorithms is called in the literature competitive analysis (Albers 2003; Borodin and El-Yaniv 1998).”
Railway Track Allocation: Models and Algorithms (2012, background): “More details about this kind of problem can be found in Grötschel, Krumke & Rambau (2001) [105], Albers & Leonardi (1999) [9], and Albers (2003) [8].”